title: Adaptive colour transformation of retinal images for stroke prediction.
abstract: Identifying lesions in the retinal vasculature using Retinal imaging is most often done on the green channel. However, the effect of colour and single channel analysis on feature extraction has not yet been studied. In this paper an adaptive colour transformation has been investigated and validated on retinal images associated with 10-year stroke prediction, using principle component analysis (PCA). Histogram analysis indicated that while each colour channel image had a uni-modal distribution, the second component of the PCA had a bimodal distribution, and showed significantly improved separation between the retinal vasculature and the background. The experiments showed that using adaptive colour transformation, the sensitivity and specificity were both higher (AUC 0.73) compared with when single green channel was used (AUC 0.63) for the same database and image features.
authors.forename: Premith
authors.initials: P
authors.lastname: Unnikrishnan
authors.forename: Behzad
authors.initials: B
authors.lastname: Aliahmad
authors.forename: Ryo
authors.initials: R
authors.lastname: Kawasaki
authors.forename: Dinesh
authors.initials: D
authors.lastname: Kumar
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D003116:Color; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012160:Retina / Q000473:pathology; D012171:Retinal Vessels / Q000473:pathology; D012680:Sensitivity and Specificity; D020521:Stroke / Q000175:diagnosis* / Q000473:pathology